Package: SAutomata
Type: Package
Title: Inference and Learning in Stochastic Automata
Version: 0.1.0
Authors@R: c(
    person("Muhammad Kashif", "Hanif",  role = c("cre","aut"),
    email = "mkashifhanif@gcuf.edu.pk"),
    person("Muhammad Umer", "Sarwar", role = "aut", email = "Mumersarwar@gcuf.edu.pk"),
    person("Rehman", "Ahmad", role = "aut", email = "rehman.ahmad777@gmail.com"),
    person("Zeeshan", "Ahmad", role = "aut", email = "zeeshan.cs822@gmail.com "),
    person("Karl-Heinz", "Zimmermann", role = "aut", email = "k.zimmermann@tuhh.de")
    )
Maintainer: Muhammad Kashif Hanif <mkashifhanif@gcuf.edu.pk>
Description: Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 2.0.0)
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-10-28 13:54:45 UTC; IBNE
Author: Muhammad Kashif Hanif [cre, aut],
  Muhammad Umer Sarwar [aut],
  Rehman Ahmad [aut],
  Zeeshan Ahmad [aut],
  Karl-Heinz Zimmermann [aut]
Repository: CRAN
Date/Publication: 2018-11-02 18:00:03 UTC
Built: R 4.6.0; ; 2026-04-25 22:09:27 UTC; unix
